Paper 13058-27
Paper 13058-27
Situational awareness on a graph: towards graph neural networks for spectrum analysis and battlefield management
On demand | Presented live 23 April 2024 
Abstract
Graph Neural Networks (GNN) were originally developed to infer relationships between objects in complex graph environments such as social networks. However, they have recently been applied to other domains which naturally support graph expression, such as hardware and software analysis. We propose to extend the application of GNNs to datasets which contain a temporal component, thus enabling GNN inference of event-driven situations involving the radio frequency (RF) spectrum. Post-battle analysis can train a GNN to identify individual subgraphs representing sequences of events. Trained GNNs can then be used in war time to infer a larger situation as a series of subgraphs are identified.
Presenter
Jeff Anderson
Integration Innovation, Inc. (United States)
Jeff Anderson is a Technologist with the Applied Sciences and Intel group at Integration, Innovation Inc. (i3), where he serves as a digital signal processing and machine learning subject matter expert for the Tactical Electronic Warfare Division at the Naval Research Laboratory. His career spans 20 years developing both digital and mixed-signal integrated circuits. He holds a Bachelor of Science in Computer Engineering from the University of South Alabama, and a Master of Science in Electrical and Computer Engineering from Johns Hopkins University. Currently a PhD candidate with the High-Performance Computing Laboratory at George Washington University, Mr. Anderson's research focuses on the reconfigurable accelerator chains in multi-domain computations.